Methods for training auto-labeling device and performing auto-labeling by using hybrid classification and devices using the same

Abstract

A method for training an auto-labeling device is provided. The method includes: (a) inputting a training image to a pre-trained feature extraction module to generate a feature, (b) inputting the feature to a pre-trained first classification module to output a first class score and a first uncertainty score, inputting the feature to a pre-trained second classification module, to output a second class score and a second uncertainty score, generating a scaled second uncertainty score by applying a scale parameter to the second uncertainty score, and then inputting the feature to a fitness estimation module to output a fitness value; and (c) (i) updating the scale parameter by using an uncertainty loss generated based on the first uncertainty score and the scaled second uncertainty score, and (ii) training the fitness estimation module by using a cross-entropy loss generated based on the uncertainty loss and the fitness value.